Dataset Information

Multi-membership gene regulation in pathway based microarray analysis.

ABSTRACT: BACKGROUND:Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway. RESULTS:We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency. CONCLUSIONS:We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

SUBMITTER: Pavlidis SP

PROVIDER: S-EPMC3189100 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Multi-membership gene regulation in pathway based microarray analysis.

Pavlidis Stelios P SP Payne Annette M AM Swift Stephen M SM

Algorithms for molecular biology : AMB 20110922

<h4>Background</h4>Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct ...[more]

PMID: 21939531

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Project description:Breast cancer is one of the most common malignant tumors with a high case-fatality rate among women. The present study aimed to investigate the effects of mesenchymal stem cells (MSCs) on breast cancer by exploring the potential underlying molecular mechanisms. The expression profile of GSE43306, which refers to MDA-MB-231 cells with or without a 1:1 ratio of MSCs, was downloaded from Gene Expression Omnibus database for differentially expressed gene (DEG) screening. The Database for Annotation, Visualization and Integrated Discovery was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs. The protein-protein interactional (PPI) network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins. The data was subsequently analyzed using molecular complex detection for sub-network mining of modules. Finally, DEGs in modules were analyzed using GO and KEGG pathway enrichment analyses. A total of 291 DEGs including 193 upregulated and 98 downregulated DEGs were obtained. Upregulated DEGs were primarily enriched in pathways including response to wounding (P=5.92×10-7), inflammatory response (P=5.92×10-4) and defense response (P=1.20×10-2), whereas downregulated DEGs were enriched in pathways including the cell cycle (P=7.13×10-4), mitotic cell cycle (P=6.81×10-3) and M phase (P=1.72 ×10-2). The PPI network, which contained 156 nodes and 289 edges, was constructed, and Fos was the hub node with the degree of 29. A total of 3 modules were mined from the PPI network. In total, 14 DEGs in module A were primarily enriched in GO terms, including response to wounding (P=4.77×10-6), wounding healing (P=6.25×10-7) and coagulation (P=1.13 ×10-7), and these DEGs were also enriched in 1 KEGG pathway (complement and coagulation cascades; P=0.0036). Therefore, MSCs were demonstrated to exhibit potentially beneficial effects for breast cancer therapy. In addition, the screened DEGs, particularly in PPI network modules, including FN1, CD44, NGF, SERPINE1 and CCNA2, may be the potential target genes of MSC therapy for breast cancer.

Project description:BackgroundThe microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods.ResultsWe describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime.ConclusionGene ARMADA provides a highly adaptable, integrative, yet flexible tool which can be used for automated quality control, analysis, annotation and visualization of microarray data, constituting a starting point for further data interpretation and integration with numerous other tools.

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Multi-membership gene regulation in pathway based microarray analysis.

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Multi-membership gene regulation in pathway based microarray analysis.

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